Style check

python/cugraph/cugraph/testing/__init__.py

@@ -11,12 +11,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from cugraph.testing.utils import (
-    RAPIDS_DATASET_ROOT_DIR_PATH, ResultSet
-)
+from cugraph.testing.utils import RAPIDS_DATASET_ROOT_DIR_PATH, ResultSet
 
 from cugraph.testing.resultset import (
     get_bfs_results,
     get_sssp_results,
     get_paths_results,
-)
+)

python/cugraph/cugraph/testing/resultset.py

@@ -33,7 +33,7 @@
     karate,
     polbooks,
 )
-from cugraph.testing import utils, ResultSet
+from cugraph.testing import utils
 
 default_results_upload_dir = Path(os.environ.get("RAPIDS_DATASET_ROOT_DIR")) / "results"
 
@@ -80,9 +80,7 @@
     Gnx = utils.generate_nx_graph_from_file(karate.get_path(), directed=dirctd)
     result = cugraph.bfs_edges(Gnx, source=7)
     cugraph_df = cudf.from_pandas(result)
-    test_bfs_results[
-        "{},{},{}".format(ds, dirctd, "nonnative-nx")
-    ] = cugraph_df
+    test_bfs_results["{},{},{}".format(ds, dirctd, "nonnative-nx")] = cugraph_df
 
 
 # =============================================================================
@@ -208,9 +206,7 @@
 test_paths_results["1,notarget,nx"] = nx.shortest_path_length(
     Gnx_DIS, source="1", weight="weight"
 )
-test_paths_results["1,notarget,cu"] = cugraph.shortest_path_length(
-    Gnx_DIS, "1"
-)
+test_paths_results["1,notarget,cu"] = cugraph.shortest_path_length(Gnx_DIS, "1")
 
 
 # serial_bfs_results = pickle.dumps(test_bfs_results)
@@ -221,9 +217,15 @@
 # pickle.dump(test_sssp_results, open("testing/sssp_results.pkl", "wb"))
 # pickle.dump(test_paths_results, open("testing/paths_results.pkl", "wb"))
 
-pickle.dump(test_bfs_results, open(default_results_upload_dir / "bfs_results.pkl", "wb"))
-pickle.dump(test_sssp_results, open(default_results_upload_dir / "sssp_results.pkl", "wb"))
-pickle.dump(test_paths_results, open(default_results_upload_dir / "paths_results.pkl", "wb"))
+pickle.dump(
+    test_bfs_results, open(default_results_upload_dir / "bfs_results.pkl", "wb")
+)
+pickle.dump(
+    test_sssp_results, open(default_results_upload_dir / "sssp_results.pkl", "wb")
+)
+pickle.dump(
+    test_paths_results, open(default_results_upload_dir / "paths_results.pkl", "wb")
+)
 
 # Example of how ResultSet is used in each individual testing script
 # my_bfs_results = ResultSet(local_result_file="bfs_results.pkl")

python/cugraph/cugraph/testing/utils.py

@@ -32,6 +32,7 @@
 
 import cugraph
 from cugraph.dask.common.mg_utils import get_client
+
 # from cugraph.experimental.datasets import default_download_dir
 
 
@@ -424,7 +425,10 @@ def compare_mst(mst_cugraph, mst_nx):
             Gnx_dict[u][v] = Gnx[u][v]
     return Gnx_dict"""
 
-default_results_download_dir = Path(os.environ.get("RAPIDS_DATASET_ROOT_DIR")) / "results"
+default_results_download_dir = (
+    Path(os.environ.get("RAPIDS_DATASET_ROOT_DIR")) / "results"
+)
+
 
 class ResultSet:
     """
@@ -459,7 +463,9 @@ def __init__(self, local_result_file=None, cloud_result_file=None):
                 # try to load results, if file doesn't exist raise pref. RuntimeError
                 raise FileNotFoundError(local_result_file)
             else:
-                with open(default_results_download_dir / local_result_file, "rb") as file:
+                with open(
+                    default_results_download_dir / local_result_file, "rb"
+                ) as file:
                     self.results = pickle.load(file)
         else:
             raise ValueError(

python/cugraph/cugraph/tests/traversal/test_bfs.py

@@ -12,10 +12,10 @@
 # limitations under the License.
 
 import gc
+
 # import random
 
 import pytest
-import pandas as pd
 import cupy as cp
 import numpy as np
 from cupyx.scipy.sparse import coo_matrix as cp_coo_matrix
@@ -267,7 +267,9 @@ def get_cu_graph_nx_results_and_params(
     # nx_values = get_bfs_results(
     #     "{},{},{},{}".format(seed, depth_limit, dataset_name, directed)
     # )
-    nx_values = bfs_results.results["{},{},{},{}".format(seed, depth_limit, dataset_name, directed)]
+    nx_values = bfs_results.results[
+        "{},{},{},{}".format(seed, depth_limit, dataset_name, directed)
+    ]
 
     return (G, dataset_path, directed, nx_values, start_vertex, depth_limit)
 
@@ -417,7 +419,9 @@ def test_bfs_nonnative_inputs_nx(
         _,
     ) = single_dataset_nxresults_startvertex_spc
 
-    cugraph_df = bfs_results.results["{},{},{}".format("karate", directed, "nonnative-nx")]
+    cugraph_df = bfs_results.results[
+        "{},{},{}".format("karate", directed, "nonnative-nx")
+    ]
     compare_func = _compare_bfs
     compare_func(cugraph_df, nx_values, start_vertex)
 

python/cugraph/cugraph/tests/traversal/test_paths.py

@@ -175,10 +175,10 @@ def test_shortest_path_length_no_target(graphs):
     cugraph_G, cupy_df = graphs
 
     cugraph_path_1_to_all = cugraph.shortest_path_length(cugraph_G, 1)
-    # nx_path_1_to_all = get_paths_results("1,notarget,nx")
-    # nx_gpu_path_1_to_all = cudf.DataFrame.from_dict(get_paths_results("1,notarget,cu"))
     nx_path_1_to_all = paths_results.results["1,notarget,nx"]
-    nx_gpu_path_1_to_all = cudf.DataFrame.from_dict(paths_results.results["1,notarget,cu"])
+    nx_gpu_path_1_to_all = cudf.DataFrame.from_dict(
+        paths_results.results["1,notarget,cu"]
+    )
     cupy_path_1_to_all = cugraph.shortest_path_length(cupy_df, 1)
 
     # Cast networkx graph on cugraph vertex column type from str to int.

python/cugraph/cugraph/tests/traversal/test_sssp.py

@@ -255,7 +255,9 @@ def test_sssp_nonnative_inputs_nx(
     gpubenchmark, single_dataset_source_nxresults, cugraph_input_type
 ):
     (_, _, _, source, nx_paths) = single_dataset_source_nxresults
-    result = sssp_results.results["nonnative_input,{},{}".format(cugraph_input_type, source)]
+    result = sssp_results.results[
+        "nonnative_input,{},{}".format(cugraph_input_type, source)
+    ]
     # ^^ should be a pd dataframe
     result = cudf.from_pandas(result)
     if np.issubdtype(result["distance"].dtype, np.integer):
@@ -355,7 +357,9 @@ def test_sssp_data_type_conversion(graph_file, source):
     dist_np = df["distance"].to_numpy()
     pred_np = df["predecessor"].to_numpy()
     cu_paths = dict(zip(verts_np, zip(dist_np, pred_np)))
-    nx_paths = sssp_results.results["nx_paths,data_type_conversion,{}".format(dataset_name)]
+    nx_paths = sssp_results.results[
+        "nx_paths,data_type_conversion,{}".format(dataset_name)
+    ]
 
     # Calculating mismatch
     err = 0